Meta-Learning in Medical Imaging

Abstract

Medical Image classification has become competitive with human experts, in some domains. However, its success seems contingent on the availability of large bodies of annotated data; and it is often difficult and expensive to acquire such datasets.
High data requirements are a general issue in modern Deep Learning, and increasing sample efficiency is one of the fundamental research problems today. Meta-learning is one of the directions taken to alleviate the need for huge datasets via learning to learn.
The goal of this project is to build on work presented in (Snell et al. 2917; Finn et al. 2017) to find approaches to image classification which are sample efficient and adaptable to Semi-Supervised Learning.

Location:

Literature

Resultant Paper

Medical Image classification has become competitive with human experts, in some domains. However, its success seems contingent on the availability of large bodies of annotated data; and it is often difficult and expensive to acquire such datasets. High data requirements are a general issue in modern Deep Learning, and increasing sample efficiency is one of the fundamental research problems today. Meta-learning is one of the directions taken to alleviate the need for huge datasets via learning to learn. The goal of this project is to build on work presented in (Snell et al. 2917; Finn et al. 2017) to find approaches to image classification which are sample efficient and adaptable to Semi-Supervised Learning.